Video surveillance is of critical concern in many areas of life. One problem with video as a surveillance tool is that it may be manually intensive to monitor. Recently, solutions have been proposed to solve some problems of automated video monitoring with the intelligent video surveillance systems. Such solutions are described, for example, in U.S. Pat. No. 6,696,945, or U.S. Published Patent Application No. 20050146605, identified above. One application of video surveillance is the detection of human beings and their behaviors. Unfortunately, the science of computer vision, which is the basis of the automated video monitoring, has limitations with respect to recognizing individual targets in fields of views of the video cameras, such as those used in residential, commercial, and home monitoring applications.
Current video surveillance systems such as described, for example, in C. Stauffer, W. E. L. Grimson, “Learning Patterns of Activity Using Real-Time Tracking,” and R. Collins, A. Lipton, H. Fujiyoshi, and T. Kanade, “Algorithms for Cooperative Multisensor Surveillance,” identified above, have two basic limitations. First, groups of targets may often be crowded together and detected as a single “blob.” The blob may be correctly labeled as “human group,” but the number of individuals in the group may not be ascertained. Second, other inanimate objects, such as, for example, furniture, strollers, and shopping carts, may generally not be disambiguated from legitimate targets (particularly in, for example, overhead camera shots). In addition, other “human detection” algorithms such as, for example, the techniques discussed in U.S. patent application Ser. No. 11/139,986, identified above, rely on more oblique camera views and specific human models to recognize humans, but might not perform well for overhead camera views.